Real-Time Scheduling methods for High Performance Signal Processing Applications on Multicore platform

High-performance signal processing applications is computational intensive, complex and large amount of data has to be processed at every instance. Now these complex algorithms combined with real-time requirements requires that we perform tasks parallel and this should be done within specified time constraints. Therefore high computational system like multicore system is needed to fulfill these requirements, now problem lies in scheduling these real time tasks in multicore system. In this thesis we have studied and compared the different scheduling algorithms available in multicore platform along with hierarchical memory architecture. We have evaluated the performance by comparing their schedulability using tasks from the HPEC benchmark suite for radar signal processing applications. Apart from the comparison described above, we have proposed a new algorithm based on the PD2 scheduling algorithm which called Hybrid PD2 for hierarchical shared cache multicore platform. We have compared the Hybrid PD2 algorithm along with other scheduling algorithms using four randomly generated task sets